Drugs typically interact with multiple targets (polypharmacology), explaining not only their side effects but also their efficacy. The aim of this proposal is to explain the mechanism for polypharmacology in terms of molecular evolution and exploit this insight to infer new signaling networks and design new drug leads. A motivating idea is that biological signaling networks have evolved to use a small vocabulary of essentially fixed, endogenous signaling molecules (serotonin, acetylcholine, estrogen, etc.). This causes proteins to have a degenerate repertoire of small molecule binding sites, which drugs 'discover' through polypharmacology. To read this metabolic code, we have developed a robust method (SEA) to measure when two proteins share similar ligands. Using it, we have shown that synthetic ligands of close to 500 non-GPCRs resemble those of 150 GPCRs and we have shown that we can accurately predict novel side-effect of existing drugs. I argue that 1) protein-metabolic interactions explain the success of ligand similarity networks, 2 ligand similarity associations largely cannot be explained by other bioinformatics networks, and 3) ligand similarity can be used to predict sets of targets that can be activated by a single synthetic ligand. To test, I will 1) use ligand similarity networks to predict and experimentally test that novel set of sequence un-related targets interact with common endogenous signaling metabolites. I will 2) quantify the functional complementarity and intersection between ligand- similarity networks and sequence-similarity, co-expression, and protein-protein interaction networks. I will 3) experimentally test that synthetic compounds and endogenous metabolites jointly activate ligand similar targets prioritizing by co-expression, and co-annotated for biological function, phenotype, and disease.